Portable AI Skills: Build Once, Deploy Across Claude, Copilot, and OpenAI

Most marketing teams build AI skills the wrong way. They create a campaign brief generator inside Claude, a lead scoring prompt in ChatGPT, and a content repurposer in Microsoft Copilot. Three platforms. Three versions of the same logic. Three maintenance burdens. When leadership switches vendors next quarter, everything gets rebuilt from zero.

Portable AI skills eliminate this waste. The core insight: 80% of any AI skill is vendor-neutral business logic. The other 20% is platform plumbing. Separate the two, and your skills deploy across Claude, Copilot, and OpenAI with minimal rework. This post gives you the architecture, the comparison, and a working example you deploy this week.

TL;DR

AI skills are made of four layers: business logic, tool connections, knowledge grounding, and host UI. The business logic layer (your instructions, rules, and output formats) ports across every vendor as plain markdown. Tool connections port through MCP, an open protocol now supported by Claude, Copilot, and OpenAI. Only knowledge grounding and host UI require vendor-specific work. Design skills with this separation, and switching AI vendors becomes a two-hour migration instead of a two-month rebuild.

Key Takeaways

• A Claude skill is a markdown file (SKILL.md) with instructions, not code, and its format is an open standard
• 80% of any AI skill (business logic + tool connections) transfers across vendors without modification
• MCP (Model Context Protocol) is the universal connector now supported by all three major AI platforms
• Microsoft Copilot uses JSON declarative agent manifests with enterprise-grade access controls
• OpenAI replaced its Assistants API with the stateless Responses API in 2025
• Portable skill architecture saves marketing teams 15 to 20 hours per week in cross-platform duplication
• Store your skill logic in version control, not inside vendor dashboards

What Problem Does Portable Skill Design Solve for Marketing Teams?

Picture this scenario. Your content team builds a competitor analysis skill in Claude. It pulls data from SEMrush, enriches it with firmographic data, and outputs a formatted brief. It took two weeks to refine the prompts, test the outputs, and nail the formatting.

Now your enterprise IT department mandates Copilot for all AI work. Your sales ops team already runs OpenAI. The competitor analysis skill needs to work in all three environments. Without portable architecture, you rebuild from scratch each time. With it, you migrate in hours.

The Real Cost of Single-Vendor Skills

Single-vendor AI skills create three compounding problems for marketing operations. Rebuilding the same skill across platforms wastes 40 to 60 hours per skill annually. Prompt drift between platforms produces inconsistent outputs across teams. Vendor switching costs escalate linearly with every new skill your team creates.

A marketing org with 15 AI skills locked to one vendor faces 600 to 900 hours of rework when switching platforms. Portable architecture reduces this to under 100 hours total.

Action item: Count the number of custom AI prompts, GPTs, and Claude projects your marketing team maintains. Multiply by 40 hours. This is your current vendor-switching exposure.

What Does a Claude Skill Look Like for Marketing Teams?

Think of a Claude skill as a recipe card for AI behavior. The card tells Claude who to be, what to know, which tools to use, and how to format the output. The recipe card is a single markdown file called SKILL.md.

The Anatomy of SKILL.md

A SKILL.md file has two parts. The frontmatter (between --- markers) holds the skill name and a description of when to activate it. The body holds the actual instructions, rules, examples, and output templates written in plain English.

Here is the frontmatter for a campaign brief skill:

---
name: campaign-brief-generator
description: Use when the user needs a campaign brief, creative brief, or launch plan. Activate for requests involving campaign strategy, audience targeting, messaging frameworks, or channel allocation.
---

The description field is the activation trigger. Claude reads it at startup and decides when to load the full instructions. This means you deploy dozens of skills without consuming context window on capabilities you are not using.

How Does Claude Load Skills Efficiently?

Claude skills use a three-level loading system optimized for context window budget. This matters because every token spent on unused instructions is a token unavailable for your actual task.

Level 1 loads only the name and description at startup, roughly 50 to 100 tokens per skill. Level 2 loads the full SKILL.md instructions when your request matches the skill description. Level 3 pulls in reference files, templates, and script outputs only during active execution.

For marketing teams, this means you maintain a library of 20+ skills (campaign briefs, competitor analysis, content calendars, ad copy, email sequences) without any of them interfering with each other.

What Goes in a Skill Directory?

A skill directory organizes supporting files around the core SKILL.md:

campaign-brief-generator/
├── SKILL.md
├── references/
│   ├── brand-voice-guide.md
│   └── icp-criteria.md
├── templates/
│   ├── brief-template.md
│   └── channel-plan-template.md
└── scripts/
    └── budget-calculator.py

SKILL.md is the only required file. The references/ folder holds brand guidelines, ICP documents, and data loaded on demand. The templates/ folder provides output blueprints. The scripts/ folder contains automation code executed in a sandbox.

Action item: Create a campaign-brief-generator/ directory with a SKILL.md file. Write instructions for your most common brief format. Test it in Claude. You now have your first portable skill.

Which Skill Components Port Across Vendors?

Every AI skill, regardless of platform, contains the same four layers. Understanding which layers port and which require rework drives every architecture decision.

Layer 1: Business Logic (Fully Portable)

Business logic is the intelligence core of your skill: the role definition, decision rules, quality criteria, and output format. A campaign brief skill’s business logic includes rules like “always include target audience, key messages, success metrics, budget allocation, and timeline” and constraints like “keep the brief under 2 pages.”

This layer lives as natural language instructions on every platform. Claude stores it in SKILL.md. Copilot stores it in the instructions field of a declarative agent manifest. OpenAI stores it in system messages or Prompts. The words are identical. Only the container changes.

The SKILL.md format is now an open standard (Apache 2.0 license) published at agentskills.io. It works across Claude Code, GitHub Copilot, Cursor, and 10+ other AI coding agents. Your markdown instructions are the most portable asset your team owns.

Layer 2: Tool Connections (Portable via MCP)

MCP (Model Context Protocol) is the universal adapter between AI assistants and external tools. Anthropic created MCP and donated it to the Agentic AI Foundation in December 2025. It now has 97 million monthly SDK downloads.

One MCP server connecting to your CRM, analytics platform, or content management system works with Claude, OpenAI, Microsoft, and Google. Build the integration once. Every AI platform consumes it through the same interface.

For marketing teams, this means your HubSpot data connector, your Google Analytics 4 reporting tool, or your SEMrush competitive data feed becomes a shared resource across every AI platform your org uses.

Layer 3: Context Grounding (Vendor-Specific)

Context grounding is how the AI retrieves and accesses your proprietary data. This is the least portable layer. Each platform has its own retrieval system.

Claude loads files from the skill directory and connects to MCP servers. Microsoft Copilot indexes SharePoint, OneDrive, Teams, Email, and Dataverse through its Semantic Index. OpenAI uses vector stores with configurable chunking and metadata filtering.

Abstracting this layer requires a vendor-neutral retrieval pipeline. Host your knowledge in a system you control, such as Supabase with pgvector or a self-hosted vector database, and expose it through an MCP server. Each vendor platform then consumes the same data the same way.

Layer 4: Host UI (Never Portable)

Host UI is the deployment surface: Claude.ai, Microsoft Teams, ChatGPT, or a custom web app. This layer never transfers. Accept it early. Design everything above it to be as thin and vendor-agnostic as possible.

The Portability Stack (Visualized)

Picture a four-layer stack from top to bottom. The top layer (Host UI) changes per vendor. The second layer (Context Grounding) needs adaptation per vendor. The third layer (Tool Connections via MCP) ports freely. The bottom layer (Business Logic as markdown) ports with zero changes.

The bottom two layers represent roughly 80% of the work in building an AI skill. The top two layers represent 20%. Portable skill design maximizes the 80% and minimizes the effort spent on the 20%.

Action item: Pick one existing AI skill your team uses. List every component: instructions, tool connections, data sources, and deployment surface. Label each component as Layer 1, 2, 3, or 4. Calculate what percentage of the skill is already portable.

How Does the Same Skill Work Inside Microsoft Copilot?

Copilot extensibility uses declarative agents packaged as standard Microsoft 365 apps. The core components parallel Claude skills, but the formats and enterprise controls differ significantly.

Copilot Declarative Agent Basics

A Copilot declarative agent uses a JSON manifest (schema v1.6) containing the agent’s identity, instructions, knowledge sources, and available actions. The instructions field holds up to 8,000 characters of natural language behavioral directives. Your campaign brief business logic from SKILL.md copies directly into this field.

Microsoft recommends structuring instructions as: role definition, scope boundaries, behavioral guidelines, knowledge source references, and guardrails. A behavior_overrides setting forces the agent to use only your provided knowledge, ignoring the general model.

Copilot’s Enterprise-Grade Knowledge Sources

Copilot declarative agents access 12 distinct knowledge source types, all governed by existing Microsoft 365 access controls. The agent only surfaces data the authenticated user already has permission to see.

Key knowledge types for marketing teams include:

• OneDriveAndSharePoint for brand assets and campaign documents
• GraphConnectors for CRM and marketing automation data
• TeamsMessages for cross-functional campaign coordination
• Email for client communication context
• WebSearch with optional site scoping for competitive intelligence

How Do Copilot API Plugins Connect External Tools?

Copilot API plugins (manifest v2.4) define tool connections with JSON Schema parameters and runtime configurations. The v2.4 schema added native MCP server support through a RemoteMCPServer runtime type.

If your marketing tools already expose MCP servers, Copilot Studio detects and synchronizes them automatically. Your HubSpot MCP server built for Claude works in Copilot without custom code.

Action item: If your org uses Microsoft 365, open Copilot Studio and create a declarative agent. Paste your Claude skill’s instructions into the instructions field. Test whether the output quality matches. Most teams see 90%+ parity on the first try.

How Does OpenAI Handle Skill-Like Capabilities?

OpenAI deprecated its Assistants API in August 2025. The replacement, the Responses API, processes a single stateless request containing model selection, instructions, input, and tool definitions. The model calls multiple tools within one request without developer intervention.

OpenAI Prompts Replace Assistants

Prompts are the closest OpenAI equivalent to a Claude SKILL.md. Created in the OpenAI dashboard, Prompts bundle model selection, system instructions, tool schemas, output format expectations, and temperature settings. They support snapshot versioning with diff and rollback.

The critical difference: Prompts exist only inside the OpenAI platform. They are not file-based or open-source. Your business logic lives in their dashboard, not in a version-controlled file you own. This is a portability risk. Always maintain a markdown master copy in your own repository.

Built-In Tools for Marketing Use Cases

The Responses API provides eight built-in tools. The most relevant for marketing teams:

• web_search for real-time competitive intelligence and trend monitoring
• file_search with vector stores for brand knowledge and campaign history retrieval
• code_interpreter for ad spend analysis and reporting dashboards
• mcp for connecting to external marketing tools via MCP servers

Setting strict: true on tool definitions enforces exact schema matching on outputs. Neither Claude nor Copilot offers this level of output schema enforcement natively.

Action item: If you maintain custom GPTs in ChatGPT, export the instruction text and save it as a SKILL.md file in your repository. You now own the logic independently of the OpenAI platform.

How Do the Three Platforms Compare for Marketing Teams?

This comparison focuses on the dimensions marketing technology leaders use to evaluate AI infrastructure:

Dimension	Claude Skills	Copilot Extensions	OpenAI Responses API
Instruction format	Markdown file (SKILL.md)	JSON manifest (8K char limit)	System messages or Prompts
Open standard	Yes, Apache 2.0	No, proprietary to M365	No, proprietary API
Marketing tool integration	MCP servers	MCP + Power Platform + Graph	MCP + function calling
CRM access	Via MCP server	Native Graph connector	Via MCP or custom function
Enterprise data governance	Workspace deployment	Full M365 RBAC + Purview	Organization sharing + Azure
Setup complexity for marketers	Low (edit a text file)	Medium (JSON + Copilot Studio)	Medium (dashboard + API)
Portability of instructions	Highest (open format)	Low (locked to M365 ecosystem)	Low (locked to OpenAI)
Best for	Teams wanting vendor flexibility	Teams deep in Microsoft 365	Teams building custom AI apps

Claude optimizes for simplicity and portability. Copilot optimizes for enterprise governance and Microsoft 365 integration depth. OpenAI falls between the two.

What Does a Portable Marketing Skill Look Like in Practice?

A lead enrichment skill demonstrates the full portability pattern. The skill takes a raw lead record from a webform submission, enriches it with firmographic data, scores it against your ICP, and recommends a next action.

The Portable Business Logic

These instructions work identically across all three platforms. Store them in version control:

SYSTEM: You are a B2B lead qualification analyst for {{COMPANY_NAME}}.

<context>
ICP criteria: {{ICP_CRITERIA}}
Lead record: {{LEAD_RECORD}}
</context>

MUST follow these rules:
1. Enrich the lead with company size, industry, tech stack, and recent funding data
2. Score the lead 1-100 against the provided ICP criteria
3. Flag missing data fields with confidence levels (high, medium, low)
4. Recommend one action: fast-track to sales, nurture via email sequence, or disqualify

Output: JSON with keys "enriched_record", "icp_score", "missing_fields", "recommendation", "reasoning"

Replace the {{VARIABLES}} with your specifics. The same prompt structure works in a Claude SKILL.md, a Copilot declarative agent instructions field, and an OpenAI Prompt.

The Portable Tool Layer

One MCP server handles all data enrichment calls: company lookup, funding data retrieval, tech stack detection, and CRM record updates. This same server connects to Claude through native MCP support, to Copilot through the RemoteMCPServer plugin runtime, and to OpenAI through the mcp built-in tool.

Build the MCP server once. Deploy it on your infrastructure. Every AI platform in your org connects to it.

The Vendor-Specific Context Layer

Claude loads your ICP criteria from the skill’s references/icp-criteria.md file. Copilot pulls it from a SharePoint document through its OneDriveAndSharePoint capability. OpenAI retrieves it from a vector store through file_search. The underlying ICP data is identical. The retrieval path differs.

Migration Timeline: Platform to Platform

Moving this lead enrichment skill from Claude to Copilot takes roughly 2 hours of work:

1. Copy the business logic from SKILL.md into the Copilot declarative agent instructions field (15 minutes)
2. Point the agent’s MCP connection to your existing enrichment server (15 minutes)
3. Configure SharePoint as the knowledge source for ICP criteria (30 minutes)
4. Test the output against your Claude baseline and adjust guardrails (60 minutes)

Without portable architecture, this same migration requires 40 to 60 hours of prompt engineering, tool reconnection, and output quality testing.

Action item: Build the lead enrichment skill above using your ICP criteria. Deploy it in Claude first. Then migrate it to one other platform your org uses. Time the migration. Your actual migration hours become the business case for portable architecture.

What Decision Framework Guides Portable Skill Design?

Four architecture decisions determine whether a new AI skill will be portable or locked to a single vendor. Answer these before writing any instructions.

Decision 1: Where Does Your Business Logic Live?

Store all skill instructions as markdown files in version control. Do not store business logic inside vendor dashboards, Copilot Studio flows, or GPT Builder interfaces. Those are deployment targets, not source-of-truth locations.

Your GitHub repository (or GitLab, Bitbucket) is the canonical source. Vendor platforms receive copies. When you update the skill, update the repository first, then push to each deployment target.

Decision 2: How Do Your Tools Connect?

Build every external tool integration as an MCP server. MCP now works across Claude, OpenAI, Microsoft, Google, and orchestration frameworks like LangChain and Microsoft Semantic Kernel. One server replaces three vendor-specific integrations.

Decision 3: Where Does Your Knowledge Live?

Abstract knowledge retrieval behind an MCP server or vendor-neutral vector database. Store source documents in a system you control. Supabase, Pinecone, or your own Postgres with pgvector all work. Expose retrieval results through MCP tools so every platform consumes the same data.

Decision 4: How Thin Is Your Host Layer?

Keep vendor-specific configuration minimal. The Claude SKILL.md, Copilot manifest, and OpenAI Prompt should reference your portable business logic and connect to your MCP tools. They should not contain unique logic unavailable to other platforms.

Portable Skill Design Checklist

Run through this checklist before building any new AI skill:

• Business logic stored as markdown in version control
• All tool connections built as MCP servers
• Knowledge retrieval abstracted behind vendor-neutral infrastructure
• Host layer contains only deployment configuration, not unique logic
• Migration path documented for at least one alternative platform
• Output quality baseline established for comparison across platforms

Action item: Print this checklist and post it where your team builds AI skills. Before any new skill gets approval, require answers to all six items.

What Are the Enterprise Implications?

Enterprise marketing teams face a strategic trade-off between depth of vendor integration and flexibility to switch. Portable skill architecture resolves this trade-off by separating the two.

Vendor Negotiation Leverage

97 million monthly MCP SDK downloads confirm strong industry convergence. The Agentic AI Foundation, co-founded by Anthropic, OpenAI, and Block under the Linux Foundation, governs MCP as shared infrastructure. Teams with portable skill libraries negotiate AI vendor contracts from a position of strength. Switching costs drop from months to days.

Cross-Team Skill Reuse

Portable AI skills turn scattered automations into shared organizational assets. A lead enrichment skill built as markdown + MCP serves the Claude-using content team, the Copilot-using sales ops team, and the OpenAI-using product team simultaneously. One skill library. Multiple deployment targets. Time saved: 15 to 20 hours per week across teams sharing the same capability set.

Future-Proofing Against Model Churn

The performance gap between open-source and proprietary models narrows every quarter. Marketing teams running portable architectures switch between Claude, GPT, Gemini, Llama, and Mistral based on cost, latency, and accuracy per task. Teams locked to one vendor’s skill format watch their options shrink while competitors optimize freely.

Action item: Present this portable skill strategy to your VP of Marketing or CTO. Frame it as infrastructure investment: one skill library, multiple deployment targets, and reduced vendor lock-in. Use the migration timeline from the lead enrichment example as proof of ROI.

Final Takeaways

Portable AI skills separate business logic (markdown), tool connections (MCP servers), context grounding (vendor-neutral retrieval), and host UI (platform-specific deployment). Design this separation from the start of every new skill.

The SKILL.md format is the most portable AI instruction standard available in 2026. It is open-source under Apache 2.0, adopted by 10+ AI coding agents, and governed through the agentskills.io specification.

MCP is the universal tool integration protocol. All three major platforms, Claude, Copilot, and OpenAI, support it natively. Build tool connections on MCP instead of vendor-proprietary formats.

Context grounding remains the hardest portability layer. Abstract your knowledge retrieval behind MCP servers or self-hosted vector databases. This prevents rebuilding retrieval pipelines during vendor migrations.

Enterprise teams building portable skill libraries gain compound returns: faster negotiations, reduced migration risk, cross-team reuse, and the freedom to pick the best model for each task. Teams still building single-vendor skills accumulate technical debt with every new automation they ship.